Abstract

We describe a novel method for propagating disparity values using directional masks and a voting scheme. The driving force of the propagation direction is image gradient, making the process anisotropic, whilst ambiguities between propagated values are resolved using a voting scheme. This kind of anisotropic densification process achieves significant density enhancement at a very low error cost: in some cases erroneous disparities are voted out, resulting not only in a denser but also a more accurate final disparity map. Due to the simplicity of the method it is suitable for embedded implementation and can also be included as part of a system-on-chip (SOC). Therefore it can be of great interest to the sector of the machine vision community that deals with embedded and/or real-time applications.

Published

Results

By using a voting scheme based on masks that are syntonized with the image gradient, we obtain considerable densification of sparse disparity maps without increasing the error. The figure below shows a set of 8 voting masks. Intensity represents the number of votes each position gets. High intensity means more votes, while low intensity means less votes. Black represents 0 votes.

By placing a mask (that best syntonizes with the image gradient direction) on top of each disparity estimation, the neighbouring pixels obtains votes for that particular disparity value. The disparity value that obtains the most votes, wins. Following figure shows results for well known image from the Middlebury database. 'C' stands for percentage correct disparities (+-1 disparity level), while 'D' means density, also given in percentage.

As we can observe from the above figure, we obtain considerable densification with the VMP (Voting Mask Propagation), while the error remains practically the same.

Conclusions

We have shown that our novel method of propagating sparse disparity information based on directional masks and a voting scheme is capable of significantly increasing density with a very minor increase in overall error, thus considerably enhancing the initial sparse disparity map. Further densification can be achieved by down-scaling, by active interpolation or by diffusion. Even though in this study we have used VMP for propagating binocular visual information based on monocular cues, VMP can also be used for propagating other visual cues, such as optical flow and others. The simplicity of the method facilitates its efficient implementation.